Functional Data Analysis and Visualisation of Three-dimensional
Surface Shape

J 2021

Functional Data Analysis and Visualisation of Three-dimensional Surface Shape

KATINA, Stanislav, Liberty VITTERT and Adrian W. BOWMAN

Basic information

Original name

Functional Data Analysis and Visualisation of Three-dimensional Surface Shape

Authors

KATINA, Stanislav (703 Slovakia, guarantor, belonging to the institution), Liberty VITTERT and Adrian W. BOWMAN

Edition

Journal of the Royal Statistical Society Series C (Applied Statistics), Wiley, 2021, 0035-9254

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10103 Statistics and probability

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 1.680

RIV identification code

RIV/00216224:14310/21:00121551

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.1111/rssc.12482

UT WoS

000647418500001

Keywords in English

asymmetry; functional data; human faces; shape; surface data; visualisation

Abstract

V originále

The advent of high resolution imaging has made data on surface shape widespread. Methods for the analysis of shape based on landmarks are well established but high resolution data require a functional approach. The starting point is a systematic and consistent description of each surface shape. Three innovative forms of analysis are then introduced. The first uses surface integration to address issues of registration, principal component analysis and the measurement of asymmetry, all in functional form. Computational issues are handled through discrete approximations to integrals, based in this case on appropriate surface area weighted sums. The second innovation is to focus on sub-spaces where interesting behaviour such as group differences are exhibited, rather than on individual principal components. The third innovation concerns the comparison of individual shapes with a relevant control set, where the concept of a normal range is extended to the highly multivariate setting of surface shape. This has particularly strong applications to medical contexts where the assessment of individual patients is very important. All of these ideas are developed and illustrated in the important context of human facial shape, with a strong emphasis on the effective visual communication of effects of interest.

Links

MUNI/A/1615/2020, interní kód MU

Name: Matematické a statistické modelování 5 (Acronym: MaStaMo5)

Investor: Masaryk University

Citovat

KATINA, Stanislav, Liberty VITTERT and Adrian W. BOWMAN. Functional Data Analysis and Visualisation of Three-dimensional Surface Shape. Journal of the Royal Statistical Society Series C (Applied Statistics). Wiley, 2021, vol. 70, No 3, p. 691-713. ISSN 0035-9254. Available from: https://dx.doi.org/10.1111/rssc.12482.

@article{1766556,
   author = {Katina, Stanislav and Vittert, Liberty and Bowman, Adrian W.},
   article_number = {3},
   doi = {http://dx.doi.org/10.1111/rssc.12482},
   keywords = {asymmetry; functional data; human faces; shape; surface data; visualisation},
   language = {eng},
   issn = {0035-9254},
   journal = {Journal of the Royal Statistical Society Series C (Applied Statistics)},
   title = {Functional Data Analysis and Visualisation of Three-dimensional Surface Shape},
   url = {https://doi.org/10.1111/rssc.12482},
   volume = {70},
   year = {2021}
}

TY  - JOUR
ID  - 1766556
AU  - Katina, Stanislav - Vittert, Liberty - Bowman, Adrian W.
PY  - 2021
TI  - Functional Data Analysis and Visualisation of Three-dimensional Surface Shape
JF  - Journal of the Royal Statistical Society Series C (Applied Statistics)
VL  - 70
IS  - 3
SP  - 691-713
EP  - 691-713
PB  - Wiley
SN  - 00359254
KW  - asymmetry
KW  - functional data
KW  - human faces
KW  - shape
KW  - surface data
KW  - visualisation
UR  - https://doi.org/10.1111/rssc.12482
N2  - The advent of high resolution imaging has made data on surface shape widespread. Methods for the analysis of shape based on landmarks are well established but high resolution data require a functional approach. The starting point is a systematic and consistent description of each surface shape. Three innovative forms of analysis are then introduced. The first uses surface integration to address issues of registration, principal component analysis and the measurement of asymmetry, all in functional form. Computational issues are handled through discrete approximations to integrals, based in this case on appropriate surface area weighted sums. The second innovation is to focus on sub-spaces where interesting behaviour such as group differences are exhibited, rather than on individual principal components. The third innovation concerns the comparison of individual shapes with a relevant control set, where the concept of a normal range is extended to the highly multivariate setting of surface shape. This has particularly strong applications to medical contexts where the assessment of individual patients is very important. All of these ideas are developed and illustrated in the important context of human facial shape, with a strong emphasis on the effective visual communication of effects of interest.
ER  -

KATINA, Stanislav, Liberty VITTERT and Adrian W. BOWMAN. Functional Data Analysis and Visualisation of Three-dimensional Surface Shape. \textit{Journal of the Royal Statistical Society Series C (Applied Statistics)}. Wiley, 2021, vol.~70, No~3, p.~691-713. ISSN~0035-9254. Available from: https://dx.doi.org/10.1111/rssc.12482.

Detailed Information on Publication Record